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Value-at-risk estimation of crude oil price using MCA based transient risk modeling approach

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  • He, Kaijian
  • Lai, Kin Keung
  • Yen, Jerome

Abstract

With the increasing level of volatility in the crude oil market, the transient data feature becomes more prevalent in the market and is no longer ignorable during the risk measurement process. Since there are multiple representations for these transient data features using a set of bases available, the sparsity measure based Morphological Component Analysis (MCA) model is proposed in this paper to find the optimal combinations of representations to model these transient data features. Therefore, this paper proposes a MCA based hybrid methodology for analyzing and forecasting the risk evolution in the crude oil market. The underlying transient data components with distinct behaviors are extracted and analyzed using MCA model. The proposed algorithm incorporates these transient data features to adjust for conservative risk estimates from traditional approach based on normal market condition during its risk measurement process. The reliability and stability of Value at Risk (VaR) estimated improve as a result of finer modeling procedure in the multi frequency and time domain while maintaining competent accuracy level, as supported by empirical studies in the representative West Taxes Intermediate (WTI) and Brent crude oil market.

Suggested Citation

  • He, Kaijian & Lai, Kin Keung & Yen, Jerome, 2011. "Value-at-risk estimation of crude oil price using MCA based transient risk modeling approach," Energy Economics, Elsevier, vol. 33(5), pages 903-911, September.
  • Handle: RePEc:eee:eneeco:v:33:y:2011:i:5:p:903-911
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    References listed on IDEAS

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    1. repec:eee:eneeco:v:66:y:2017:i:c:p:523-534 is not listed on IDEAS
    2. He, Kaijian & Yu, Lean & Lai, Kin Keung, 2012. "Crude oil price analysis and forecasting using wavelet decomposed ensemble model," Energy, Elsevier, vol. 46(1), pages 564-574.
    3. Julio Alonso Cifuentes & Andrés Arcila Vásquez, 2012. "Un modelo de predicciones diarias para contratos de futuros de azúcar," REVISTA ECONOMÍA & REGIÓN, UNIVERSIDAD TECNOLÓGICA DE BOLÍVAR, vol. 6(2), pages 33-51, December.
    4. Ghorbel, Ahmed & Trabelsi, Abdelwahed, 2014. "Energy portfolio risk management using time-varying extreme value copula methods," Economic Modelling, Elsevier, vol. 38(C), pages 470-485.
    5. Jamshed Y. Uppal & Syeda Rabab Mudakkar, 2014. "Mitigating Vulnerability to Oil Price Risk— Applicability of Risk Models to Pakistan’s Energy Problem," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 53(3), pages 293-308.
    6. Kaijian He & Hongqian Wang & Jiangze Du & Yingchao Zou, 2016. "Forecasting Electricity Market Risk Using Empirical Mode Decomposition (EMD)—Based Multiscale Methodology," Energies, MDPI, Open Access Journal, vol. 9(11), pages 1-11, November.

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